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Author

Nebojsa Bacanin

Other affiliations: Megatrend University
Bio: Nebojsa Bacanin is an academic researcher from Singidunum University. The author has contributed to research in topics: Metaheuristic & Computer science. The author has an hindex of 25, co-authored 121 publications receiving 1740 citations. Previous affiliations of Nebojsa Bacanin include Megatrend University.


Papers
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Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.

167 citations

Journal ArticleDOI
TL;DR: This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint and proves to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
Abstract: Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

111 citations

Journal ArticleDOI
TL;DR: Modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm are introduced based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions.
Abstract: Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay’s ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering

96 citations

Journal ArticleDOI
TL;DR: Comparison with other state-of-the-art optimization metaheuristics including genetic algorithms, simulated annealing, tabu searc h and particle swarm optimization shows that the proposed algorithm is superior considering quality of the portfolio optimization results, especially mean Euclidean distance from the standard efficiency frontier.
Abstract: Portfolio selection (optimization) problem is a very important and widely rese arched problem in the areas of finance and economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to the cardinality constrained mean-variance (CCMV) portfolio optimization model. By analyzing ABC metaheuristic, some deficiencies such as slow convergence to the optimal region, were noticed. In this paper ABC algorithm improved by hybridization with the firefly algorithm (FA) is presented. FA's search procedure was incorporate d into the ABC algorithm to enhance the process of exploitation. We tested our proposed algorithm on standard test data used in the literature. Comparison with other state-of-the-art optimization metaheuristics including genetic algorithms, simulated annealing, tabu searc h and particle swarm optimization (PSO) shows that our approach is superior considering quality of the portfolio optimization results , especially mean Euclidean distance from the standard efficiency frontier.

94 citations

Journal ArticleDOI
TL;DR: This paper introduced modifications to the seeker optimization algorithm to control exploitation/exploration balance and hybridized it with elements of the firefly algorithm that improved its exploitation capabilities and outperformed other state-of-the-art swarm intelligence algorithms.

92 citations


Cited by
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Journal ArticleDOI
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

1,645 citations

22 Jan 2014
TL;DR: The review of studies using empirical, data-driven methodologies to identify clusters of diet, PA and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity suggests that obesogenic cluster patterns are complex with a mixed PA/sedentary behavior cluster observed most frequently, but healthy and unhealthy patterning of all three behaviors was also reported.
Abstract: This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis and latent class analysis, to identify clustering patterns of diet, physical activity and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity.

370 citations

Journal ArticleDOI
01 Oct 2014
TL;DR: Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability.
Abstract: Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances.

248 citations

Journal ArticleDOI
TL;DR: A comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm.

216 citations